Search Results (642)

Background: Soybean (Glycine max (L.) Merr.), a nutrient-rich leguminous crop high in protein, lipids, and minerals, is extensively cultivated worldwide. The chemical composition of soybean seeds depends not only on the genetic characteristics of the cultivar but also on environmental conditions and agricultural practices. In recent years, biostimulants have gained increasing importance in crop production due to their ability to enhance physiological processes in plants and potentially influence nutrient accumulation. This study aimed to investigate how cultivar and biostimulant type influence the chemical composition of soybean seeds under varying weather conditions in Central Europe. Methods: A three-year field experiment (2017–2019) was conducted in eastern Poland (Central Europe) using a split-plot design. The experimental factors included three non-GMO soybean cultivars (Abelina, Merlin, and SG Anser) and two foliar biostimulants (Asahi SL and Improver). In addition to classical ANOVA, the multivariate analysis of the impact of the investigated factors included principal component analysis (PCA). Results: The applied factors significantly affected seed contents of fat, protein, dry matter, ash, fibre, and macronutrients (N, P, K). Cv. Merlin had the highest fat (22.65%) and fibre content (9.33%), while Abelina showed the highest protein (37.06%) and dry matter content (94.42%). Biostimulant application increased the accumulation of several seed components. Asahi SL significantly enhanced fat content (by 0.69%), protein content (by over 1.5%), and dry matter content (by nearly 0.2%) compared to the control. Improver was more effective in increasing nitrogen (by 0.24%), phosphorus (by 0.5%), and potassium (by 0.15%) contents. Weather conditions throughout the growing seasons significantly altered the impact of the biostimulants. The PCA analysis revealed distinct relationships among the chemical properties of seeds, meteorological factors, and the applied biostimulants. Full article

The objective of this study was to evaluate the temporal trend of suicide deaths and the disparities in the occurrence of suicide death between Indigenous and non-Indigenous populations. This ecological study analyzed deaths from intentionally self-inflicted injuries in the southern region of Brazil (states of Paraná, Santa Catarina, and Rio Grande do Sul) from 2010 to 2019. The variables analyzed included annual frequency of events, age, sex, marital status, and education level. Descriptive analysis, association measures, and verification of temporal trends were performed. The average age-standardized suicide mortality rate for both populations was approximately 9.0 per 100,000 inhabitants, with a predominance among males (80%), single individuals (65%), and youth (37%). When only the state of Paraná was considered, the mortality rate during the period was 12.41 among the Indigenous population versus 6.94 per 100,000 inhabitants in the non-Indigenous population. In this state, the probability of suicide death was found to be almost twice as high among Indigenous men and women, with 13 times greater odds of death among Indigenous children and youth. A statistically significant temporal increase in the number of cases was observed only in the non-Indigenous population. An annual seasonal pattern of events among Indigenous individuals was suggested. The findings indicate a high suicide rate among the Indigenous population, particularly in Paraná, especially among young, single individuals, with a growing trend over the years. Full article

Peatlands are vital for global carbon cycling, and their ecological functions are influenced by vegetation composition. Accurate vegetation mapping is crucial for peatland management and conservation, but traditional methods face limitations such as low spatial resolution and labor-intensive fieldwork. We used ultra-high-resolution UAV imagery captured across seasonal and topographic gradients and assessed the impact of phenology and topography on classification accuracy. Additionally, this study evaluated the performance of four deep learning models (ResNet, Swin Transformer, ConvNeXt, and EfficientNet) for mapping vegetation in a subtropical Sphagnum peatland. ConvNeXt achieved peak accuracy at 87% during non-growing seasons through its large-kernel feature extraction capability, while ResNet served as the optimal efficient alternative for growing-season applications. Non-growing seasons facilitated superior identification of Sphagnum and monocotyledons, whereas growing seasons enhanced dicotyledon distinction through clearer morphological features. Overall accuracy in low-lying humid areas was 12–15% lower than in elevated terrain due to severe spectral confusion among vegetation. SHapley Additive exPlanations (SHAP) of the ConvNeXt model identified key vegetation indices, the digital surface model, and select textural features as primary performance drivers. This study concludes that the combination of deep learning and UAV imagery presents a powerful tool for peatland vegetation mapping, highlighting the importance of considering phenological and topographical factors. Full article

Temperate grasslands are highly sensitive to climate change and play a crucial role in terrestrial carbon cycling. In the context of global warming, heterotrophic respiration (Rh) has intensified, contributing significantly to atmospheric CO₂ emissions. However, seasonal patterns of Rh, particularly differences between the growing season (GS) and non-growing season (non-GS), remain poorly quantified. This study used daily eddy covariance data from multiple flux towers combined with MODIS GPP and NPP products to estimate Rh across temperate grasslands from 2002 to 2021. We examined interannual variations in GS and non-GS Rh contributions and assessed their relationships with key hydrothermal variables. The results showed that mean Rh during GS and non-GS was 527 ± 357 and 341 ± 180 g C m⁻² yr⁻¹, respectively, accounting for 57.8 ± 14.6% and 42.2 ± 14.6% of the annual Rh. Moreover, GS Rh exhibited a declining trend, while non-GS Rh increased over time, indicating a gradual convergence in their seasonal contributions. This pattern was primarily driven by increasing drought stress in GS and warmer, moderately moist conditions in non-GS that favored microbial activity. Our findings underscore the necessity of distinguishing seasonal Rh dynamics when investigating global carbon cycle dynamics. Future earth system models should place greater emphasis on seasonal differences in soil respiration processes by explicitly incorporating the influence of soil moisture on the decomposition rate of soil organic carbon, in order to improve the accuracy of carbon release risk assessments under global change scenarios. Full article

Air pollution poses growing challenges to public health and urban mobility in Southeast Asia. This study investigates how air quality crises affect travel mode choices in Chiang Rai, Thailand, a secondary city experiencing seasonal PM2.5 smog episodes. A structured online survey was conducted with 406 respondents, collecting paired data on travel behaviors during non-air quality crisis (N-AQC) and air quality crisis (AQC) periods. Using a multinomial logit model (MNL), key socioeconomic and trip-related variables were analyzed to estimate mode choice probabilities. The results reveal significant behavioral shifts during an air quality crisis, with private car usage increasing from 30.30% to 34.70% and motorcycle usage decreasing from 50.20% to 42.90%. Multinomial logit models attained correct classification rates of 67.5% and 63.8%, with pseudo R² values exceeding 0.50 for both periods. These findings highlight how environmental stress alters travel behavior, especially among younger and low-income populations. The study contributes new insights from a Southeast Asian urban context, emphasizing the need for adaptive transport policies, protective infrastructure, and equity-focused interventions to promote sustainable mobility during an environmental crisis. Full article

This study investigates the microclimate characteristics and temporal variations in carbon and water fluxes in the naturally restored plots in Puding County, Anshun City, Guizhou Province. Continuous monitoring and collection of environmental factors through the eddy covariance system reveals the dynamic change patterns of the microclimate environment in the early stages of returning farmland to forest and the dynamic characteristics of carbon and water fluxes in the ecosystem. The results show that the study area exhibits a pronounced carbon sink capacity at the annual scale. The net ecosystem exchange (NEE) gradually increased with the extension of the recovery time, and the carbon sink intensity reached its peak during the growing season (June–August). The water use efficiency (WUE) was significantly higher in the growing season compared to the non-growing season, indicating that the WUE and carbon sequestration capacity increased simultaneously. Structural equation model (SEM) analysis revealed that latent heat flux has the most significant regulatory effect on carbon sink function. Furthermore, temperature, relative humidity, and latent heat flux have a significant positive regulatory effect on NEE (p < 0.001), while rain and soil heat flux affect carbon sink function through an indirect path. By constructing a complex meteorological carbon flux relationship network, the main regulatory mechanism of the ecosystem carbon sink in the Puding area was revealed. These findings confirm that the implementation of returning farmland to forest has effectively enhanced the carbon sequestration capacity of karst ecosystems. Full article

Traditional NDVI-based biomass estimation methods often suffer from saturation at high vegetation density and limited sensitivity during early crop growth, which reduces their effectiveness for precise monitoring. This study addresses these limitations by introducing the use of the second derivative of NDVI with respect to near-infrared (NIR) reflectance as a novel indicator of inflection points and dynamic changes in crop development. The proposed method is mathematically derived, and a corresponding index is calculated. Field trials were conducted on five winter wheat varieties over two growing seasons (2021–2023). The results demonstrated a strong correlation between the derived index and actual biomass measurements. To validate the findings, linear regression analysis between the second derivative of NDVI and biomass scores yielded R and R² values equal to 1. These findings confirm the high predictive power and reliability of the method for non-destructive UAV-based biomass monitoring in precision agriculture. Full article

Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs between high-yielding ponded and aerobic rice, (ii) validate the feasibility of using the squared simplified canopy chlorophyll content index (SCCCI²) for N uptake estimates, and (iii) explore the SCCCI² and similar chlorophyll-sensitive indices for grain quality monitoring. Multispectral images were collected from an unmanned aerial vehicle during both rice-growing seasons. Above-ground biomass and nitrogen (N) uptake were measured at panicle initiation (PI). The performance of single-vegetation-index models in estimating rice N uptake, as previously published, was assessed. Yield and grain quality were determined at harvest. Results showed that canopy reflectance in the visible and near-infrared regions differed between aerobic and ponded rice early in the growing season. Chlorophyll-sensitive indices showed lower values in aerobic rice than in the ponded rice at PI, despite having similar yields at harvest. The SCCCI² model (RMSE = 20.52, Bias = −6.21 Kg N ha⁻¹, and MAPE = 11.95%) outperformed other models assessed. The SCCCI², squared normalized difference red edge index, and chlorophyll green index correlated at PI with the percentage of cracked grain, immature grain, and quality score, suggesting that grain milling quality parameters could be associated with N uptake at PI. This study highlights canopy reflectance differences between high-yielding aerobic (averaging 15 Mg ha⁻¹) and ponded rice at key phenological stages and confirms the validity of a single-vegetation-index model based on the SCCCI² for N uptake estimates in ponded and non-ponded rice crops. Full article

False cleavers (Galium spurium L.) is a broadleaf weed species that affects wheat productivity because of its strong competition for resources. It has developed resistance to acetolactate synthase (ALS) inhibitors, such as sulfonylureas and triazolopyrimidines, which are herbicides widely used in durum wheat. Integrated weed management programs can contribute to the control of this species and delay the evolution of herbicide resistance. Thus, a two-year field experiment was conducted to evaluate the effects of sowing time, variety, and herbicides on crop yield, density, and dry weight of a false cleavers population with resistance to ALS inhibitors. In both growing seasons, a split-split-plot design was used with three replicates. The sowing date was chosen as the main plot factor, durum wheat varieties as the subplot factor, and herbicides as the sub-subplot factor. The herbicide treatments were: (1) metsulfuron-methyl/bensulfuron-methyl (4/50 g a.i. ha⁻¹), (2) aminopyralid/florasulam (9.9/4.95 g a.i. ha⁻¹), (3) pyroxsulam and florasulam/2,4-D (18.75 + 4.725/225 g a.i. ha⁻¹), (4) 2,4-D/bromoxynil (633.15/601.2 g a.i. ha⁻¹), non-treated control, and hand-weeded control for the first season, while in the second season one more herbicide treatment (halauxifen-methyl/florasulam, 5.6/5.15 g a.i. ha⁻¹) was added. Herbicide application was performed on 10 March 2021 and 28 March 2022, when the crop was at the end of tillering and the beginning of stem elongation. The results showed that the density of false cleavers was not affected by the variety or sowing time. However, its dry weight was 17.3–23.4% higher in early sowing (16 November in 2020 and 8 November 2021) than in late sowing (24 December 2020 and 2 December 2021). Among the herbicides tested, 2,4-D/bromoxynil and halauxifen-methyl/florasulam effectively controlled false cleavers, showing greater efficacy in late sowing (>88%), which ultimately led to a higher yield. In conclusion, our two-year findings demonstrate that delayed sowing as part of an integrated weed management strategy can contribute to controlling resistant populations of false cleavers to ALS-inhibiting herbicides without affecting the quantity and quality of durum wheat yield in areas with a Mediterranean climate. Full article

The practice in agriculture of spreading polyethylene (PE) film over the soil surface as mulch is a common, global practice that aids in conserving water, increasing crop yields, suppressing weed growth, and decreasing growing time. However, these films are typically only used for a single growing season, and thus, their use and non-biodegradability come with some serious environmental consequences due to their persistence in the soil and potential for microplastic pollution, particularly when retrieval and disposal options are poor. On the microscale, particles < 5 mm from degraded films have been observed to disrupt soil structure, impede water and nutrient cycling, and affect soil organisms and plant health. On the macroscale, there are obvious and serious environmental consequences associated with the burning of plastic film and its leakage from poorly managed landfills. To maintain the crop productivity afforded by mulching with PE film while avoiding the environmental downsides, the development and use of biodegradable polymer technologies is being explored. Here, the efficacy of a newly developed, water-dispersible, sprayable, and biodegradable polyester–urethane–urea (PEUU)-based polymer was compared with two commercial PE mulches, non-degradable polyethylene (NPE) and OPE (ox-degradable polyethylene), in a greenhouse tomato growth trial. Water savings and the effects on plant growth and soil characteristics were studied. It was found that PEUU provided similar water savings to the commercial PE-based mulches, up to 30–35%, while showing no deleterious effects on plant growth. The results should be taken as preliminary indications that the sprayable, biodegradable PEUU shows promise as a replacement for PE mulch, with further studies under outside field conditions warranted to assess its cost effectiveness in improving crop yields and, importantly, its longer-term impacts on soil and terrestrial fauna. Full article

Wheat leaf rust (Lr), caused by Puccinia triticina Eriks. (Pt), is one of the most important diseases affecting wheat production worldwide. Using resistant wheat cultivars is the most economic and environmentally friendly way to control leaf rust. The Chinese wheat cultivar Xinong1163-4 has shown good resistance to Lr in field trials. To identify the genetic basis of Lr resistance in Xinong1163-4, 195 recombinant inbred lines (RILs) from the Xinong1163-4/Thatcher cross were phenotyped for Lr severity in three environments: the 2017/2018, 2018/2019, and 2019/2020 growing seasons in Baoding, Hebei Province. Bulked segregant analysis and simple sequence repeat markers were then used to identify the quantitative trait loci (QTLs) for Lr adult plant resistance (APR) in the population. As a result, six QTLs were detected, designated as QLr.hbau-1BL.1, QLr.hbau-1BL.2, and QLr.hbau-1BL.3. These QTLs were predicted to be novel. QLr.hbau-4BL, QLr.hbau-4BL.1, and QLr.hbau-3A were identified at similar physical positions to previously reported QTLs. Based on chromosome positions and molecular marker testing, QLr.hbau-1BL.3 shares similar flanking markers with Lr46. Lr46 is a non-race-specific APR gene for leaf rust, stripe rust, and powdery mildew. Similarly, QLr.hebau-4BL showed resistance to multiple diseases, including leaf rust, stripe rust, Fusarium head blight, and powdery mildew. The QTLs identified in this study, as well as their closely linked markers, can potentially be used for marker-assisted selection in wheat breeding. Full article

Amid global environmental change, the intensification of nitrogen (N) deposition exerts critical impacts on the growth of forest vegetation and the structure and function of ecosystems in subtropical China. However, the physiological and growth response mechanisms of subtropical tree species remain poorly understood. This study explored adaptive mechanisms of typical subtropical tree species to N deposition, analyzing biomass accumulation, root plasticity, and nutrient/photosynthate allocation strategies. One-year-old potted seedlings of Phyllostachys edulis (moso bamboo) and Cunninghamia lanceolata (Chinese fir) were subjected to four N-addition treatments (N0: 0, N1: 6 g·m⁻²·a⁻¹, N2: 12 g·m⁻²·a⁻¹, N3: 18 g·m⁻²·a⁻¹) for one year. In July and December, measurements were conducted on seedling organ biomass, root morphological and architectural traits, as well as nutrient elements (N and phosphorus(P)) and non-structural carbohydrate (soluble sugars and starch) contents in roots, stems, and leaves. Our results demonstrate that the Chinese fir exhibits stronger tolerance to N deposition and greater root morphological plasticity than moso bamboo. It adapts to N deposition by developing root systems with a higher finer root (diameter ≤ 0.2 mm) ratio, lower construction cost, greater branching intensity and angle, and architecture approaching dichotomous branching. Although N deposition promotes short-term biomass and N accumulation in both species, it reduces P and soluble sugars contents, leading to N/P imbalance and adverse effects on long-term growth. Under conditions of P and photosynthate scarcity, the Chinese fir preferentially allocates soluble sugars to leaves, while moso bamboo prioritizes P and soluble sugars to roots. In the first half of the growing season, moso bamboo allocates more biomass and N to aboveground parts, whereas in the second half, it allocates more biomass and P to roots to adapt to N deposition. This study reveals that Chinese fir enhances its tolerance to N deposition through the plasticity of root morphology and architecture, while moso bamboo exhibits dynamic resource allocation strategies. The research identifies highly adaptive root morphological and architectural patterns, demonstrating that optimizing the allocation of elements and photosynthates and avoiding elemental balance risks represent critical survival mechanisms for subtropical tree species under intensified N deposition. Full article

Evapotranspiration (ET) is an important part of agricultural water consumption, yet little is known about nocturnal evapotranspiration (ETN) patterns. An eddy covariance system was used to observe ET over five consecutive years (2020–2024) during the growing season in a dry farming area of the Loess Plateau. Daytime and nocturnal evapotranspiration were partitioned using the photosynthetically active radiation threshold to reveal the changing characteristics of ETN at multiple time scales and its control variables. The results showed the following: (1) In contrast to the non-significant trend in ETN on the diurnal and daily scales, monthly ETN dynamics exhibited two peak fluctuations during the growing season. (2) The contribution of ETN to ET exhibited seasonal characteristics, being relatively low in summer, with interannual variations ranging from 10.9% to 14.3% and an annual average of 12.8%. (3) The half-hourly ETN, determined by machine learning methods, was driven by a combination of factors. The main driving factors were the difference between surface temperature and air temperature (Ts-Ta) and net radiation (Rn), which have almost equivalent contributions. Regression analysis results suggested that Ta was the main factor influencing ETN/ET at the monthly scale. This study focuses on the nighttime water loss process in dry farming fields in Northwest China, and the results provide a basis for rational allocation and efficient utilization of agricultural water resources in arid regions. Full article

Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R² < 0.1), likely due to complete areal coverage with dense grass. The LiDAR canopy height models performed better for all the samples (R² = 0.37), with much stronger performance (R² = 0.81) when using a stratified model to predict the RDM in plots with predominantly standing (as opposed to laying) vegetation. This study demonstrates the potential of UAV LiDAR for direct RDM quantification where vegetation is standing upright, which could help improve RDM mapping and management for rangelands in California and beyond. Full article

Soybean (Glycine max L.) is vulnerable to environmental stresses, such as heavy rainfall and high winds, which promote lodging and reduce plant performance during the monsoon season. To mitigate these issues, we evaluated the effects of plant topping, a practice involving the removal of apical buds, on plant architecture, physiological traits, and grain yield in four soybean cultivars over two growing seasons (2021–2022). Plant topping was performed at the V6-7 stage by cutting 30–35 cm above the ground. Plant topping reduced plant height by up to 23.5% and decreased leaf area index (by 8.0–16.4%), potentially improving light penetration into the lower canopy. Although chlorophyll concentration declined temporarily (297.8 vs. 272.8 mg m⁻² for non-topping vs. topping, respectively), NDVI remained stable, indicating delayed senescence. Chlorophyll fluorescence parameters revealed cultivar-specific stress responses, particularly in Taegwang, which showed elevated ABS/RC, TR₀/RC, and DI₀/CS values under plant topping. Grain yield was generally unaffected, except in Jinpung, which increased by 34% under plant topping in 2021 (2701 kg ha⁻¹ vs. 3621 kg ha⁻¹ for non-topping vs. topping). In conclusion, plant topping may help improve canopy structure and light distribution without compromising yield, potentially reducing lodging risk and offering a cultivar-specific management strategy. Full article